Finding a target term in an audio corpus is one of the fundamental problems in automatic speech processing. Given the vast amount of existing spoken information, there is an increasing need for small indices and fast search. Typically, known spoken term detection (STD) systems search for terms in an index built from the output of an automatic speech recognition (ASR) system. The ASR output representation is the 1-best hypothesis, and using it for indexing results in good STD performance if the ASR system has low word error rate. However, many known STD systems, which may have to deal with degraded inputs, can benefit from using a richer ASR output representation. Lattices and confusion networks (CNs) are two used representations of multiple hypotheses from an ASR system, and have been used for building STD indices. The lattice approach requires large disk space to store an index. Although CNs require less disk space, CN computation can be prohibitive for large lattices.